A unifying primary framework for quantum graph neural networks from quantum graph states
This work addresses the challenge of developing coherent quantum machine learning models for graph-based data, though it appears incremental by building on existing quantum graph state concepts.
The authors tackled the problem of unifying quantum graph neural networks by showing that quantum graph states can serve as a foundational framework, enabling their use as parameterized quantum circuits or underlying structures for constructing these networks on quantum computers.
Graph states are used to represent mathematical graphs as quantum states on quantum computers. They can be formulated through stabilizer codes or directly quantum gates and quantum states. In this paper we show that a quantum graph neural network model can be understood and realized based on graph states. We show that they can be used either as a parameterized quantum circuits to represent neural networks or as an underlying structure to construct graph neural networks on quantum computers.